Navigating a graph is a general problem if you only know the local connections. There has even been written scientific articles about it, e.g., Jon Kleinberg‘s Navigating in a small world. When a human (such as I) navigate a social graph such as the co-author graph of scientific articles one can utilize auxillary information, here the information about where a researcher has worked, what his/her interest are and how prominent the researcher is (how many co-authors s/he has). As Portman worked from Harvard a good guess would be to start looking among my co-authors that are near Harvard. Nicholas Lange is from Harvard and we collaborated in the American funded Human Brain Project. I knew that radiology professor Bruce R. Rosen was/is a central figure in Boston MRI researcher, so I thought that there might be a productive connection from him, — both to Lange and to Portman. Portman’s co-author Baird is professor and has written some neuroimaging papers, so among Portman’s co-authors Baird was probably the one that could lead to a path. While searching among Lange and Baird co-authors I confused Bruce Rosen and Bruce Cohen (their Hamming distance is not great). This error proved fertile.

If I didn’t run into Cohen and really wanted to find a path between Portman and me then I think a more automated and brute force method could have been required. One way would be to query PubMed and put the co-author graph into NetworkX which is a Python package. It has a shortest path algorithm. Joe Celko in his book SQL for Smarties: Advanced SQL programming shows a shortest path algorithm in SQL. That might be an alternative to NetworkX.